Methods and systems for adding content to images based on negative space recognition

ABSTRACT

Embodiments for adding content to images are provided. A plurality of images are received. Each of the plurality of images includes a plurality of image portions. A negative space score is calculated for each of the plurality of image portions. At least some of the plurality of image portions are selected to display content based on the calculated negative space scores. The plurality of images are caused to be rendered with the content displayed over the selected at least some of the plurality of image portions.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates in general to computing systems, and moreparticularly, to various embodiments for adding content to images basedon negative space recognition.

Description of the Related Art

Various types of images are often rendered in such a way to displaycontent other than what is shown in the images themselves. For example,videos (e.g., television broadcasts, movies, etc.) are sometimespresented with “captions” or “closed captioning” that includestext-based versions of sounds associated with the videos (e.g.,dialogue, descriptions of sound effects, names of songs, etc.) and/orother content, such as advertisements, logos, etc. In some instances,this content obscures portions of the image(s) that the viewer wouldlike to see (e.g., statistics related to a professional athlete or teamwhen viewing a sporting event).

Current solutions to this issue include, for example, disabling thedisplaying of the content and/or customizing the manner in which thecontent is displayed. However, current methods for customizing thedisplaying of the content do not provide a flexible, dynamic solutionthat adapts to particular scenarios (e.g., the types, sizes, locations,etc. of subjects in the images) and generally results in the contentbeing shown in a relatively stationary manner. As a result, the“important” or “desired” portions of the images are still often obscuredfrom view.

SUMMARY OF THE INVENTION

Various embodiments for adding content to images, by a processor, areprovided. A plurality of images are received. Each of the plurality ofimages includes a plurality of image portions. A negative space score iscalculated for each of the plurality of image portions. At least some ofthe plurality of image portions are selected to display content based onthe calculated negative space scores. The plurality of images are causedto be rendered with the content displayed over the selected at leastsome of the plurality of image portions.

In addition to the foregoing exemplary embodiment, various other systemand computer program product embodiments are provided and supply relatedadvantages. The foregoing Summary has been provided to introduce aselection of concepts in a simplified form that are further describedbelow in the Detailed Description. This Summary is not intended toidentify key features or essential features of the claimed subjectmatter, nor is it intended to be used as an aid in determining the scopeof the claimed subject matter. The claimed subject matter is not limitedto implementations that solve any or all disadvantages noted in thebackground.

BRIEF DESCRIPTION OF THE DRAWINGS

In order that the advantages of the invention will be readilyunderstood, a more particular description of the invention brieflydescribed above will be rendered by reference to specific embodimentsthat are illustrated in the appended drawings. Understanding that thesedrawings depict only typical embodiments of the invention and are nottherefore to be considered to be limiting of its scope, the inventionwill be described and explained with additional specificity and detailthrough the use of the accompanying drawings, in which:

FIG. 1 is a block diagram depicting an exemplary computing nodeaccording to an embodiment of the present invention;

FIG. 2 is an additional block diagram depicting an exemplary cloudcomputing environment according to an embodiment of the presentinvention;

FIG. 3 is an additional block diagram depicting abstraction model layersaccording to an embodiment of the present invention;

FIG. 4 is a block diagram of a method and/or system for adding contentto images according to an embodiment of the present invention;

FIG. 5 is a plan view of an exemplary received image according to anembodiment of the present invention;

FIG. 6 is a plan view of the image of FIG. 5 divided into multipleportions;

FIG. 7 is a plan view of the image of FIG. 6 with content displayed overselected portions;

FIG. 8 is a plan view of an exemplary second received image with contentdisplayed over selected portions thereof according to an embodiment ofthe present invention

FIG. 9 is a plan view of the image of FIG. 8 with the content displayedin an intermediate position according to an embodiment of the presentinvention;

FIG. 10 is a plan view of an exemplary control panel according to anembodiment of the present invention; and

FIG. 11 is a flowchart diagram of an exemplary method for adding contentto images according to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE DRAWINGS

As discussed above, various types of images are often rendered in such away to display content other than what is shown in the imagesthemselves. For example, videos (e.g., television broadcasts, movies,etc.) are sometimes presented with “captions” or “closed captioning”that includes text-based versions of sounds associated with the videos(e.g., dialogue, descriptions of sound effects, names of songs, etc.)and/or other content, such as advertisements, logos, etc. In someinstances, this content is displayed in such a way that it obscuresportions of the image(s) that the viewer would like to see.

For example, consider a scenario in which a viewer is watching (orviewing, consuming, etc.) a television broadcast of a live event, suchas an awards show or a sporting event. If the broadcast is beingprovided with closed captioning (e.g., perhaps as an optional featureenabled by the user), various types of information that are displayed aspart of the broadcast, such as other awards a nominee has won orstatistics related to a particular athlete or team, may be (at leastpartially) obscured (or “blocked”) by the addition of the closedcaptioning to the images (or video frames).

Similarly, when the viewer is watching a movie, even if this “extra”content (e.g., captioning or advertisements) is shown near an edge ofthe image(s), it may obscure portions of the image that are consideredto be “important.” For example, the content creator may intentionallyshow an important detail in such a way that it is not centered in theframe.

Current solutions to this issue include, for example, disabling thecontent and/or customizing the manner in which the content is displayed.However, current methods for customizing the displaying of the contentdo not provide a flexible, dynamic solution that adapts to particularscenarios (e.g., the types, sizes, locations, etc. of subjects in theimages) and generally results in the content being shown in a relativelystationary manner. As a result, the important or desired portions of theimages are still often obscured from view.

To address these needs and/or the shortcomings in the prior art, in someembodiments described herein, methods and/or systems are disclosed that,for example, analyze images to be rendered (e.g., footage, videos,etc.), and when the images are rendered, display content (e.g.,captions, advertisements, etc.) in such a way to optimize the viewingexperience for the user (e.g., to minimize the extent to whichimportant/desirable portions of the images are obscured). In someembodiments, this process is performed automatically, in a dynamicmanner such that the viewing is optimized regardless of the image(s)being rendered.

In some embodiments, this process is performed by analyzing the received(and/or “to be rendered”) image(s) with respect to “negative space.” Aswill be appreciated by one skilled in the art, negative space may beconsidered to be relatively “empty” space (or portions of an image)around the subject(s) of the image, such as the background or otherpart(s) of the image which the primary subject(s) do not occupy (i.e.,the “unimportant” portion(s) of the image). For example, if an imageshows an individual (e.g., regardless of their position in the image)with a blue sky or patterned wallpaper behind/surrounding theindividual, the portion(s) of the image that show the sky or wallpapermay be considered to be negative space. In contrast, “positive space”may be considered to by the portion(s) of the image that are occupied bythe subjects (e.g., the individual(s)), even if they are not necessarilycentered in the image (i.e., the “important” portion(s) of the image).

For example, in some embodiments, a series of images (or at least oneimage), such as a video (or video clip) that includes multiple videoframes, is received (or retrieved, detected, etc.), perhaps before beingrendered (e.g., shown on a display, such as a television, computingdevice display screen, etc.). The system divides each of the images intosmaller portions that are then processed (e.g., via visual recognitionor computer vision software and/or a cognitive analysis) to determinewhich of the portions are (or include) negative space and which of theportions are positive space (e.g., the system classifies each of theportions as either negative space or positive space). For example, thesystem may calculate a negative space score (or negative spaceconfidence score) for each of the portions.

The system then selects one or more of the portions (e.g., based on thecalculated scores) of the image(s) to display “additional” content(e.g., captions, advertisements, etc.). The image(s) are then rendered(or caused to be rendered) on a suitable display device with the contentbeing displaying in (or on, over, etc.) the selected portion(s) of theimage(s). The result may be that the content is displayed on/over theimage(s) in such a way that its position (and/or size, shape, etc.) isnot static but flexible and dynamic as the images are being consumed (orviewed) by the user(s) (or viewer(s)), and the viewing experience of theuser is maximized or optimized.

In some embodiments, additional features are implemented to furtherenhance the user's viewing experience. For example, because of thedifferent images rendered, the basic method described above may resultin the content (e.g., captions) “jumping” or “darting” around the images(or display screen) during rendering. That is, when utilized with avideo, under some circumstances (e.g., when there are significantchanges between one video frame to the next), the content may appear tomove a relatively large distance across the screen very quickly. Such aneffect may make it difficult or frustrating for the user to “follow” thecontent. To account for this, in some embodiments, the system imposes a“limit” with respect to how far the content can appear to move betweensuccessive images (e.g., video frames).

Additionally, in some embodiments, the manner in which the content isrendered is adjusted or tuned based on, for example, the color tones,brightness, etc. of the images (or portions of the images) over whichthe content is displayed to further enhance the viewing experience. Thisfeature may prevent, as one example, black captioning being displayedover dark portions of the image(s) (e.g., the color and/or brightness ofthe captioning may be adjusted to it is visible against the darkportions of the image(s)). Also, a similar functionality may beimplemented that allows content (e.g., advertisements, logos,watermarks, etc.) to be “blended” with the selected portions of theimages. For example, if the content includes a logo, the color of aportion of the logo may be adjusted to “blend in” with the selectedportions of the images (e.g., so that the logo is still visible but notoverly distracting). Such features may be implemented by analyzing theportions of the images (and/or the content) using computing visiontechniques and/or a cognitive analysis.

As such, in some embodiments, the methods and/or systems describedherein may utilize a “cognitive analysis,” “cognitive system,” “machinelearning,” “cognitive modeling,” “predictive analytics,” and/or “dataanalytics,” as is commonly understood by one skilled in the art.Generally, these processes may include, for example, receiving and/orretrieving multiple sets of inputs, and the associated outputs, of oneor more systems and processing the data (e.g., using a computing systemand/or processor) to generate or extract models, rules, etc. thatcorrespond to, govern, and/or estimate the operation of the system(s),or with respect to the embodiments described herein, adding content toimages. Utilizing the models, the performance (or operation) of thesystem (e.g., utilizing/based on new inputs) may be predicted and/or theperformance of the system may be optimized by investigating how changesin the input(s) effect the output(s). Feedback received from (orprovided by) users and/or administrators may also be utilized, which mayallow for the performance of the system to further improve withcontinued use.

The processes described herein may utilize various information or datasources associated with users (e.g., users who provide search queries)and/or the content (e.g., the document(s), image(s)). With respect tousers, the data sources may include, for example, any available datasources associated with the user. For example, in some embodiments, aprofile (e.g., a cognitive profile) for the user(s) may be generated.Data sources that may be use used to generate a cognitive profile forthe user(s) may include any appropriate data sources associated with theuser that are accessible by the system (perhaps with the permission orauthorization of the user). Examples of such data sources include, butare not limited to, communication sessions and/or the content (orcommunications) thereof (e.g., phone calls, video calls, text messaging,emails, in person/face-to-face conversations, etc.), a profile of (orbasic information about) the user (e.g., medical history, job title,place of work, length of time at current position, family role, etc.), aschedule or calendar (i.e., the items listed thereon, time frames,etc.), projects (e.g., past, current, or future work-related projects),location (e.g., previous and/or current location and/or locationrelative to other users), social media activity (e.g., posts, reactions,comments, groups, etc.), browsing history (e.g., web pages visited), andonline purchases. As a particular example, a user's medical history(e.g., with respect to eyesight/vision) may be utilized to adjust/tunethe color/brightness and/or size of displayed content.

It should be understood that as used herein, the term “computing node”(or simply “node”) may refer to a computing device, such as a mobileelectronic device or a desktop computer, and/or an application, such achatbot, an email application, a social media application, a webbrowser, etc. In other words, as used herein, examples of computingnodes include, for example, computing devices such as mobile phones,tablet devices, desktop computers, or other devices, such as appliances(IoT appliances) that are owned and/or otherwise associated withindividuals (or users), and/or various applications that are utilized bythe individuals on such computing devices.

In particular, in some embodiments, a method for adding content toimages, by a processor, is provided. A plurality of images are received.Each of the plurality of images includes a plurality of image portions.A negative space score is calculated for each of the plurality of imageportions. At least some of the plurality of image portions are selectedto display content based on the calculated negative space scores. Theplurality of images are caused to be rendered with the content displayedover the selected at least some of the plurality of image portions.

The plurality of images may include a plurality of video frames. Thecausing of the plurality of images to be rendered may include causing afirst of the plurality of images to be rendered with the contentdisplayed over a first of the plurality of image portions, and after therendering of the first of the plurality of images, causing a second ofthe plurality of images to be rendered with the content displayed over asecond of the plurality of image portions. A distance between a positionof the content on the first of the plurality of images and a position ofthe content on the second of the plurality of images may be limited.

The content may include at least one of captions associated with theplurality of images and an advertisement associated with the pluralityof images. The calculating of the negative space score for each of theplurality of image portions may be performed utilizing a cognitiveanalysis.

A color tone of the selected at least some of the plurality of imageportions (and/or the content) may be analyzed. At least one of abrightness and a color of the content may be adjusted based on theanalyzing of the color tone of the selected at least some of theplurality of image portions.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment, such ascellular networks, now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 (and/or one ormore processors described herein) is capable of being implemented and/orperforming (or causing or enabling) any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,system memory 28 may include at least one program product having a set(e.g., at least one) of program modules that are configured to carry outthe functions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in system memory 28 by way of example, and not limitation,as well as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

In the context of the present invention, and as one of skill in the artwill appreciate, various components depicted in FIG. 1 may be locatedin, for example, personal computer systems, server computer systems,thin clients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, mobile electronic devices such asmobile (or cellular and/or smart) phones, personal data assistants(PDAs), tablets, wearable technology devices, laptops, handheld gameconsoles, portable media players, etc., as well as computing systems invehicles, such as automobiles, aircraft, watercrafts, etc. However, insome embodiments, some of the components depicted in FIG. 1 may belocated in a computing device in, for example, a satellite, such as aGlobal Position System (GPS) satellite. For example, some of theprocessing and data storage capabilities associated with mechanisms ofthe illustrated embodiments may take place locally via local processingcomponents, while the same components are connected via a network toremotely located, distributed computing data processing and storagecomponents to accomplish various purposes of the present invention.Again, as will be appreciated by one of ordinary skill in the art, thepresent illustration is intended to convey only a subset of what may bean entire connected network of distributed computing components thataccomplish various inventive aspects collectively.

Referring now to FIG. 2, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, cellular (or mobile) telephone orPDA 54A, desktop computer 54B, laptop computer 54C, and vehicularcomputing system (e.g., integrated within automobiles, aircraft,watercraft, etc.) 54N may communicate.

Still referring to FIG. 2, nodes 10 may communicate with one another.They may be grouped (not shown) physically or virtually, in one or morenetworks, such as Private, Community, Public, or Hybrid clouds asdescribed hereinabove, or a combination thereof. This allows cloudcomputing environment 50 to offer infrastructure, platforms and/orsoftware as services for which a cloud consumer does not need tomaintain resources on a local computing device. It is understood thatthe types of computing devices 54A-N shown in FIG. 2 are intended to beillustrative only and that computing nodes 10 and cloud computingenvironment 50 can communicate with any type of computerized device overany type of network and/or network addressable connection (e.g., using aweb browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Device layer 55 includes physical and/or virtual devices, embedded withand/or standalone electronics, sensors, actuators, and other objects toperform various tasks in a cloud computing environment 50. Each of thedevices in the device layer 55 incorporates networking capability toother functional abstraction layers such that information obtained fromthe devices may be provided thereto, and/or information from the otherabstraction layers may be provided to the devices. In one embodiment,the various devices inclusive of the device layer 55 may incorporate anetwork of entities collectively known as the “internet of things”(IoT). Such a network of entities allows for intercommunication,collection, and dissemination of data to accomplish a great variety ofpurposes, as one of ordinary skill in the art will appreciate.

Device layer 55 as shown includes sensor 52, actuator 53, “learning”thermostat 56 with integrated processing, sensor, and networkingelectronics, camera 57, controllable household outlet/receptacle 58, andcontrollable electrical switch 59 as shown. Other possible devices mayinclude, but are not limited to, various additional sensor devices,networking devices, electronics devices (such as a remote controldevice), additional actuator devices, so called “smart” appliances suchas a refrigerator, washer/dryer, or air conditioning unit, and a widevariety of other possible interconnected devices/objects.

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provides cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provides pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and, in the context of the illustratedembodiments of the present invention, various workloads and functions 96for adding content to images, as described herein. One of ordinary skillin the art will appreciate that the workloads and functions 96 may alsowork in conjunction with other portions of the various abstractionslayers, such as those in hardware and software 60, virtualization 70,management 80, and other workloads 90 (such as data analytics processing94, for example) to accomplish the various purposes of the illustratedembodiments of the present invention.

As previously mentioned, in some embodiments, methods and/or systems foradding content to images are provided. In some embodiments, the systemanalyzes images (or at least one image) to be rendered (e.g., footage,videos, etc.), and when the images are rendered, displays content (e.g.,captions, advertisements, etc.) over the images in such a way tooptimize the viewing experience for the user (e.g., to minimize theextent to which important/desirable portions of the images areobscured). In some embodiments, this process is performed automaticallyand in a dynamic manner such that the viewing is optimized regardless ofthe image(s) being rendered. In some embodiments, this process isperformed by determining which portions(s) of the image(s) are/includenegative space (at least relative to other portions of the images),which are then utilized to display content when the images are rendered.

FIG. 4 illustrates a block diagram of an exemplary method (and/orsystem) 400 for adding content to images according to some embodimentsdescribed herein. It should be understood that the steps depicted inFIG. 4 are merely intended as an example of such a method, as in otherembodiments, different steps and/or a different order of the steps maybe utilized.

At block 402, the computing system (and/or software application)performing the method 400 receives and stores one or more images, suchas a video or video clip that includes multiple video frames or one ormore static images. For example, a user may upload the image(s) to thesystem and/or the system may receive the images from another system(e.g., via the Internet, television broadcast, etc.), which are thenstored on any suitable memory device (e.g., a memory in a computingdevice or “smart” television).

At block 404, image portions are (or one or more image portion is)extracted from the image(s). For example, the system may divide each ofthe images into multiple portions (or image portions). This process maybe performed using tile localization in which the images are “chopped”into smaller images (or tiles). The number (and/or size) of the imageportions may be adjustable/configurable. For example, the user may setvalues (e.g., via a user interface, preferences functionality, etc.) forthe number of rows and/or columns of portions/tiles each image isdivided into. In some embodiments, if no user input is provided in thisregard, a default value may be utilized, which may be automaticallyset/adjusted based on the size of the images. At block 406, the imageportions are stored along with data associated with the images fromwhich they are extracted (e.g., which image, the location of theportion/tile within the image, etc.).

At block 408, a negative space score (or negative space confidencescore) is calculated for each of the image portions. The calculatedscores may be determined as numerical values (e.g., whole numbers,decimals, positive/negative, etc.) or “grades” (e.g., “high,” “low,”etc.). The process may include analyzing the image portions utilizing acomputer vision technique and/or cognitive analysis. In someembodiments, a machine learning model is utilized, which has beentrained to recognize negative space and/or differentiate (or classify)negative space and positive space.

At block 410, the calculated scores for the image portions are mapped totheir respective images (and/or locations/positions within therespective images) and at least some of the image portions are selected(e.g., based on the calculated scores). In some embodiments, the imageportion(s) with the highest negative spaces score(s) are selected. Inother words, the portion(s) of the image(s) that the system determinesto have the highest probability of including/being negative space areselected.

In some embodiments, the selected portions are utilized to displaycontent (e.g., captions, advertisements, etc.) as the images arerendered (e.g., the video is played on a computing device, television,etc.). However, in some embodiments, the position and/or perceivedmovement(s) of the content is limited (e.g., when multiple images arerendered in succession, such as when a video is played). For example,still referring to FIG. 4, at block 412, it is determined whether or notsuch a limitation is to be implemented, which may be based on whether ornot the user has enabled such functionality (e.g., via a preferencesfunctionality, system settings, control panel, etc.).

If such functionality is not enabled, at block 414, the content isplaced (or positioned) over (or on) the selected portions of theimage(s), and the images are rendered at block 416. That is, regardlessof whether or not a particular image is the first in a series of images(e.g., a video) and previous images (e.g., a previous video frame) hadsimilar content added over certain portions thereof, the content isplaced on the portion(s) of the image with the highest negative spacescore(s). As described above, although this may result in the contentbeing displayed on portions of the images that are not “important,” thecontent may be appear to “jump” around when multiple images are renderedin succession (e.g., when a video is being played).

However, if such functionality is enabled, at block 418, the position ofcontent displayed over previous images is taken into consideration (insuch instances/if appropriate), which is used to tune/adjust theposition of the content by limiting its perceived movement from oneimage to the next, and at block 416, the image(s) are then rendered withthe content appropriately positioned.

For example, if two successive video frames are rendered with captions,and in the first frame, the captions are displayed in the lower, rightcorner of the frame (and/or display screen), and the system determinesthat the upper, left corner of the second image has the highestprobability of being negative space, rather than display the captions inthe upper, left corner of the second frame, the system may select anintermediate position/area of the second frame (e.g., between the lower,right corner and the upper, left corner) to display the captions inorder to limit/prevent the captions from “darting” across the displayscreen between the two video frames. When multiple, successive imagesare process/analyzed by the system, such as in the case of videos/videoframes, this process may be repeated from one image/frame to the nextsuch that, if appropriate, the captions (or other content) will seem to“migrate” or “smoothly” transition across the images/screen (assuming nochanges in the negative space of the images that would affect theplacement of the content occur).

The extent to which the content (e.g., captions) is prevented frommoving from one frame to the next may be based on, for example, the sizeof the area occupied by the content (e.g., length, width, etc.). Forexample, in some embodiments, the position of the content is preventedfrom changing more than a particular percentage of the length and/orwidth of the size of the area it occupies from one video frame to thenext. This maximum allowed movement may be set by the user (e.g., asetting of between 0 and 100 or 0% and 100%).

In some embodiments, the manner in which the content is rendered (e.g.,brightness, color, etc.) is adjusted or tuned based on, for example, thecolor tones, brightness, etc. of the images (or portions of the images)over which the content is displayed (and/or the color, brightness, etc.of the content) to further enhance the viewing experience. This featuremay prevent, as one example, white captioning being displayed over lightportions of the image(s) (e.g., the color and/or brightness of thecaptioning may be adjusted to it is visible against the light portionsof the image(s)). Also, a functionality may be implemented that allowscontent (e.g., advertisements, logos, watermarks, etc.) to be “blendedin” with the selected portions of the images. For example, if thecontent includes a logo, the color of a portion of the logo may beadjusted to it blends in with the selected portions of the images (e.g.,so that the logo is still visible but not overly distracting). Suchfeatures may be implemented by analyzing the portions of the images(and/or the content) using computing vision techniques and/or acognitive analysis.

FIG. 5 illustrates an exemplary image 500 that is received by thesystem(s) described herein. The image 500 may be, for example, a singleframe of a video (e.g., a movie, television show, commercial, etc.) towhich captions (or other content) is to be added before the video isrendered or played. In the depicted embodiment, the image 500 shows anindividual talking on a phone while standing in front of a historicbuilding, with a lawn or grassy area between, including a plaque on theground near the individual.

As described above, in some embodiments, the image 500 is divided intomultiple portions or image portions, as depicted in FIG. 6. In theexample shown, the image 500 is divided into twenty image portions 502,which are of equal size and arranged in a grid-like pattern. It shouldbe understood that in other embodiments the analyzed images may bedivided into different numbers of portions, sizes of portions, portionsof different shapes, etc. (e.g., based on user preferences).

Still referring to FIG. 6, in some embodiments, a negative space scoreis calculated for each of the portions 502. In the depicted embodiment,it should be assumed that portions 504, 506, and 508 (i.e., particularones of the portions 502 that correspond to portions of the lawn/grassin the image 500) have been determined to have the highest negativespace scores and/or be the most likely to include/be negative space(although in the particular image shown, the portions of the image 500that correspond to the sky above the building may also be determined tohave high negative spaces scores and/or be appropriate for displayingcontent).

As such, when the image 500 is rendered, content 510 is displayed in (oron) portions 504, 506, and 508, as shown in FIG. 7. In the particularexample shown, the content 510 includes a caption or caption box (e.g.,dialogue including a question, “What time do you close?”). However, asdescribed above, other types of content may be utilized, such asadvertisements, logos, etc., or combinations thereof (e.g., a captioncombined with an advertisement).

Additionally, in some embodiments, computer vision techniques (e.g.,object detection) and/or a cognitive analysis (and/or informationassociated with the images, such as tags, metadata, etc.) may beutilized to recognize, identify, and/or classify one or more entity(e.g., individuals, locations, objects, etc.) appearing in the images.In such instances, at least some of the content displayed may beselected based on recognized entities (i.e., the content may beassociated with the identified entities). For example, in the exampleshown in FIG. 7, the content may include an advertisement associatedwith the building/location shown and/or the individual appearing in theforeground.

In such instances, the system may calculate or determine a score (orgrade) for a recognized entity (or entities) with respect to how“important” the entity is in/to the image(s) (e.g., an entity importancescore). The calculation of the entity importance score may be based on,for example, the prominence of the entity within the image(s), thenumber of such entities within the image(s), the position of the entitywithin the image (e.g., whether or not the entity is relatively centeredin the image), and/or information associated with the images, such astags, metadata, etc. For example, if an image is determined to beinclude one or more of a particular type of animal (e.g., dogs) thatoccupy 80% of the image (or some other number/percentage over apredetermined threshold), the content may include an advertisement (orsome other type of content) associated with the recognized type ofanimal (or other type of entity). In this way, the entity importancescore may be utilized to determine at least some of the content that isdisplayed. Additionally, the recognition of such entities (and/or thecalculated entity importance score) may also be utilized to tune theplacement (or position) of the content (e.g., to ensure the content doesnot block/obscure the entities in the image(s)).

Still referring to FIG. 7, it should be noted that the content 510 isdisplayed over portions of the image 500 that may be considered to beunimportant to the overall subject matter of the image (i.e., thelawn/grass), as opposed to, for example, being centered, near the bottomedge of the image 500, which would most likely result in the content 510obscuring the individual and/or the plaque shown in the image 500.

Referring now to FIG. 8, a second image 800 is shown. The second image800 may be considered to include a video frame subsequent to the frameshown in image 500 (FIGS. 5-7). In particular, the second image 800 mayinclude a video frame that appears immediately after the video frameshown in image 500 (e.g., the video may cut from image 500 directly toimage 800). Although not shown in detail, the second image 800 may beanalyzed/processed in a manner similar to that described above,resulting in content 802 being displayed as shown in FIG. 8 (e.g., onthe portions of image 800 that correspond to the sky above thebuilding). In the example shown, the content 802 includes a caption(e.g., “7 PM on weekdays,” dialogue that includes a response to thequestion asked in content 510 in FIG. 7). Although the content 802 isdisplayed in a portion(s) of image 800 that may be considered to includenegative space, it should be noted that the position of content 802 issignificantly different than that of content 510 in FIG. 7. In otherwords, in the depicted embodiment, the content (e.g., caption(s)) has“jumped” a considerable distance across the image(s)/display screenbetween the two video frames.

In contrast, in the example shown in FIG. 9, the position/movement ofcontent 804 has been limited in a manner similar to that describedabove. In particular, rather than displaying content 802 in the portionsof image 800 that correspond to the sky above the building, the systemhas displayed the content 802 in an intermediate position between wherethe content is displayed in FIG. 7 and where it is shown in FIG. 8 (asis evident from comparing FIGS. 7, 8, and 9). That is, the perceivedmovement of the content 802 has been limited. In some embodiments, ifappropriate (e.g., in a video in which the negative space in the framesdoes not change for a sufficient period), as the video continues to beplayed, content 802 is “migrated” in direction 804, towards the positionshown in FIG. 8 (i.e., over the course of multiple video frames) so thatit is eventually displayed over the portion(s) of image 800 with, forexample, the highest negative space scores, as described above.

Referring now to FIG. 10, an exemplary control panel (or user interface)1000 is shown. The control panel 1000 may be utilized by a user toadjust/control some of the aspects of functionality described herein.For example, the control panel 1000 may be rendered on a display device(e.g., of a computing device or television) in (or during, part of,etc.) a user preferences or system configuration functionality. In theexample shown, the control provided is with respect to the displaying ofcaptions (or closed captioning) while videos (e.g., movies, televisionshows, etc.) are played. However, it should be understood that thecontrol panel 1000 (and/or another, similar control panel) may also beutilized to control the displaying of other types of content, such asadvertisements, logos, watermarks, etc., as described above. In theexample shown, the control panel 1000 includes a dynamic text contrastcontrol 1002, a smooth transition control 1004, a split adjustmentcontrol 1006, and a smooth transition—caption distance calculationcontrol 1008.

The dynamic text contrast control 1002 may be utilized (e.g., via a“button” or binary “slider”) to enable/disable the dynamicdimming/brightening (and/or changes to color) of captions to ensure thecaptions are visible to the viewer given different color tones (orbrightness levels) of the utilized negative space portion(s) of theimage(s), as described above. The smooth transition control 1004 may beutilized (e.g., via a button binary slider) to enable/disable thelimiting of the positions/movements of captions with respect to theirposition on previously rendered images (e.g., to prevent/limit captionjumping/darting), as described above.

The split adjustment control 1006 may be utilized (e.g., via text/numberboxes for rows and columns) to set/adjust the number (and/or size) ofthe image portions into which the system divides each image for thenegative space calculations, as described above. For example, as thenumber of rows/columns is increased, the number of image portions maysimilarly increase (and/or each image portion may be reduced in size).The smooth transition—caption distance calculation control 1008 may beutilized (e.g., via a slider) to set/adjust the extent to which thecaption (or other content) is limited in movement from one image (orframe) to the next). For example, as this value is increased, thedistance between the positions of the caption box from one image to thenext may be reduced (i.e., assuming the negative space of the image(s)has changed such that repositioning the caption is appropriate).

Turning to FIG. 11, a flowchart diagram of an exemplary method 1100 foradding content to images provided. The method 1100 begins (step 1102)with, for example, a system (and/or software application) such asdescribed above being implemented within one or more devices, such as acomputing device, smart television, etc.

A plurality of images are (or at least one images is) received (step1104). Each of the plurality of images may include and/or be dividedinto a plurality of image portions. The plurality of images may includea plurality of video frames.

A negative space score is calculated for each of the plurality of imageportions (step 1106). The calculating of the negative space score foreach of the plurality of image portions may be performed utilizing acognitive analysis (and/or computer vision technique), as describedabove.

At least some of the plurality of image portions are selected to displaycontent based on the calculated negative space scores (step 1108). Thecontent may include, for example, at least one of captions associatedwith the plurality of images and an advertisement.

The plurality of images are caused to be rendered with the contentdisplayed over the selected at least some of the plurality of imageportions (step 1110). The causing of the plurality of images to berendered may include causing a first of the plurality of images to berendered with the content displayed over a first of the plurality ofimage portions, and after the rendering of the first of the plurality ofimages, causing a second of the plurality of images to be rendered withthe content displayed over a second of the plurality of image portions.A distance between a position of the content on the first of theplurality of images and a position of the content on the second of theplurality of images may be limited, as described above.

In some embodiments, a color tone (and/or brightness) of the selected atleast some of the plurality of image portions (and/or the content) maybe analyzed. At least one of a brightness and a color of the content maybe adjusted based on the analyzing of the color tone of the selected atleast some of the plurality of image portions.

Method 1100 ends (step 1112) with, for example, the completion of therendering of the plurality of images. The user may then provide input(or feedback) with respect to the displaying of the content, which maybe utilized to improve the performance of the system over time. Thefeedback may be explicit (e.g., received via adjustments made to thesettings) or implicit (e.g., detected by monitoring the user and/orcommunications associated with the user).

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowcharts and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowcharts and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowcharts and/or block diagram block orblocks.

The flowcharts and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowcharts or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustrations, and combinations ofblocks in the block diagrams and/or flowchart illustrations, can beimplemented by special purpose hardware-based systems that perform thespecified functions or acts or carry out combinations of special purposehardware and computer instructions.

1. A method for adding content to images comprising: receiving aplurality of images, wherein each of the plurality of images comprises aplurality of image portions; calculating a negative space score for eachof the plurality of image portions, the negative space score indicativeof identified negative space within each of the plurality of imageportions, wherein the negative space is representative of empty spacewithin sub-portions of each of the plurality of image portions aroundwhich a primary subject of the plurality of image portions does notoccupy when rendered; selecting at least some of the plurality of imageportions to display content based on said calculated negative spacescores; and causing the plurality of images to be rendered with thecontent displayed over said selected at least some of the plurality ofimage portions.
 2. The method of claim 1, wherein the plurality ofimages includes a plurality of video frames.
 3. The method of claim 1,wherein the causing of the plurality of images to be rendered includes:causing a first of the plurality of images to be rendered with thecontent displayed over a first of the plurality of image portions; andafter the rendering of the first of the plurality of images, causing asecond of the plurality of images to be rendered with the contentdisplayed over a second of the plurality of image portions.
 4. Themethod of claim 3, further comprising limiting a distance between aposition of the content on the first of the plurality of images and aposition of the content on the second of the plurality of images.
 5. Themethod of claim 1, wherein the content includes at least one of captionsassociated with the plurality of images and an advertisement associatedwith the plurality of images.
 6. The method of claim 1, furthercomprising: analyzing a color tone of said selected at least some of theplurality of image portions; and adjusting at least one of a brightnessand a color of the content based on the analyzing of the color tone ofsaid selected at least some of the plurality of image portions.
 7. Themethod of claim 1, wherein the calculating of the negative space scorefor each of the plurality of image portions is performed utilizing acognitive analysis.
 8. A system for adding content to images comprising:a processor executing instructions stored in a memory device, whereinthe processor: receives a plurality of images, wherein each of theplurality of images comprises a plurality of image portions; calculatesa negative space score for each of the plurality of image portions, thenegative space score indicative of identified negative space within eachof the plurality of image portions, wherein the negative space isrepresentative of empty space within sub-portions of each of theplurality of image portions around which a primary subject of theplurality of image portions does not occupy when rendered; selects atleast some of the plurality of image portions to display content basedon said calculated negative space scores; and causes the plurality ofimages to be rendered with the content displayed over said selected atleast some of the plurality of image portions
 9. The system of claim 8,wherein the plurality of images includes a plurality of video frames.10. The system of claim 8, wherein the causing of the plurality ofimages to be rendered includes: causing a first of the plurality ofimages to be rendered with the content displayed over a first of theplurality of image portions; and after the rendering of the first of theplurality of images, causing a second of the plurality of images to berendered with the content displayed over a second of the plurality ofimage portions.
 11. The system of claim 10, wherein the processorfurther limits a distance between a position of the content on the firstof the plurality of images and a position of the content on the secondof the plurality of images.
 12. The system of claim 8, wherein thecontent includes at least one of captions associated with the pluralityof images and an advertisement associated with the plurality of images.13. The system of claim 8, wherein the processor further: analyzes acolor tone of said selected at least some of the plurality of imageportions; and adjusts at least one of a brightness and a color of thecontent based on the analyzing of the color tone of said selected atleast some of the plurality of image portions.
 14. The system of claim8, wherein the calculating of the negative space score for each of theplurality of image portions is performed utilizing a cognitive analysis.15. A computer program product for adding content to images, by aprocessor, the computer program product embodied on a non-transitorycomputer-readable storage medium having computer-readable program codeportions stored therein, the computer-readable program code portionscomprising: an executable portion that receives a plurality of images,wherein each of the plurality of images comprises a plurality of imageportions; an executable portion that calculates a negative space scorefor each of the plurality of image portions, the negative space scoreindicative of identified negative space within each of the plurality ofimage portions, wherein the negative space is representative of emptyspace within sub-portions of each of the plurality of image portionsaround which a primary subject of the plurality of image portions doesnot occupy when rendered; an executable portion that selects at leastsome of the plurality of image portions to display content based on saidcalculated negative space scores; and an executable portion that causesthe plurality of images to be rendered with the content displayed oversaid selected at least some of the plurality of image portions.
 16. Thecomputer program product of claim 15, wherein the plurality of imagesincludes a plurality of video frames.
 17. The computer program productof claim 15, wherein the causing of the plurality of images to berendered includes: causing a first of the plurality of images to berendered with the content displayed over a first of the plurality ofimage portions; and after the rendering of the first of the plurality ofimages, causing a second of the plurality of images to be rendered withthe content displayed over a second of the plurality of image portions.18. The computer program product of claim 17, wherein thecomputer-readable program code portions further include an executableportion that limits a distance between a position of the content on thefirst of the plurality of images and a position of the content on thesecond of the plurality of images.
 19. The computer program product ofclaim 15, wherein the content includes at least one of captionsassociated with the plurality of images and an advertisement associatedwith the plurality of images.
 20. The computer program product of claim15, wherein the computer-readable program code portions further include:an executable portion that analyzes a color tone of said selected atleast some of the plurality of image portions; and an executable portionthat adjusts at least one of a brightness and a color of the contentbased on the analyzing of the color tone of said selected at least someof the plurality of image portions.
 21. The computer program product ofclaim 15, wherein the calculating of the negative space score for eachof the plurality of image portions is performed utilizing a cognitiveanalysis.